{"title":"12导联心电图的深度多标签多实例分类","authors":"Yingjing Feng, E. Vigmond","doi":"10.22489/CinC.2020.095","DOIUrl":null,"url":null,"abstract":"As part of the PhysioNet/Computing in Cardiology Challenge 2020, we developed an end-to-end deep neural network model based on 1D ResNet and an attention-based multi-instance classification (MIC) mechanism, named as MIC-ResNet, requiring minimal signal preprocessing, for identifying 27 cardiac abnormalities from 12-lead ECG data. Our team, ECGLearner, achieved a challenge validation score of 0.486 and a full test score of 0.001, placing us 33 out of 41 in the official ranking of this year's challenge.","PeriodicalId":407282,"journal":{"name":"2020 Computing in Cardiology","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Deep Multi-Label Multi-Instance Classification on 12-Lead ECG\",\"authors\":\"Yingjing Feng, E. Vigmond\",\"doi\":\"10.22489/CinC.2020.095\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As part of the PhysioNet/Computing in Cardiology Challenge 2020, we developed an end-to-end deep neural network model based on 1D ResNet and an attention-based multi-instance classification (MIC) mechanism, named as MIC-ResNet, requiring minimal signal preprocessing, for identifying 27 cardiac abnormalities from 12-lead ECG data. Our team, ECGLearner, achieved a challenge validation score of 0.486 and a full test score of 0.001, placing us 33 out of 41 in the official ranking of this year's challenge.\",\"PeriodicalId\":407282,\"journal\":{\"name\":\"2020 Computing in Cardiology\",\"volume\":\"64 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Computing in Cardiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22489/CinC.2020.095\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Computing in Cardiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22489/CinC.2020.095","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
摘要
作为PhysioNet/Computing in Cardiology Challenge 2020的一部分，我们开发了一个基于1D ResNet的端到端深度神经网络模型和一个基于注意力的多实例分类(MIC)机制，称为MIC-ResNet，需要最少的信号预处理，用于从12导联心电图数据中识别27个心脏异常。我们的团队ECGLearner获得了0.486的挑战验证分数和0.001的完整测试分数，在今年挑战的41个官方排名中排名第33位。
Deep Multi-Label Multi-Instance Classification on 12-Lead ECG
As part of the PhysioNet/Computing in Cardiology Challenge 2020, we developed an end-to-end deep neural network model based on 1D ResNet and an attention-based multi-instance classification (MIC) mechanism, named as MIC-ResNet, requiring minimal signal preprocessing, for identifying 27 cardiac abnormalities from 12-lead ECG data. Our team, ECGLearner, achieved a challenge validation score of 0.486 and a full test score of 0.001, placing us 33 out of 41 in the official ranking of this year's challenge.
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